‘Automate the back office’ is too broad to evaluate. ‘Prepare a draft service request from an incoming email’ has a clearer boundary. That smaller task makes it possible to decide where a language model helps, where ordinary code is sufficient, and where a person should stay involved.
Map the work before choosing the model
Write down the input, the required output, and the systems involved. For a service request, the model might extract the customer’s description and propose a category. Deterministic code can check required fields, look up the account, and validate allowed category values.
Anthropic’s guidance on effective agents distinguishes predefined workflows from systems in which the model directs its own steps. Use that distinction to decide how much flexibility the task actually needs. A predictable sequence often benefits from explicit control flow.
Put review where the consequence changes
A draft can be checked before it becomes a customer-visible message or an operational commitment. Show the reviewer the original request, extracted fields, and unresolved ambiguities together. Avoid requiring them to reconstruct the model’s work across several tools.
Define what happens when a customer cannot be identified or two requests appear to conflict. Route the item to a named queue with its context intact. A model-generated confidence statement is not enough to decide that an irreversible action is safe.
Measure the whole task
Compare total handling time, correction effort, and missed details with the existing process. Include the time spent reviewing drafts and resolving exceptions. Keep the original input and an appropriate audit record of approved changes, subject to retention and access controls. Expand automation only when evidence supports a specific next step, such as automatically routing an already reviewed request, rather than granting broad access to more systems.
